Modern Data Historians

A data historian records and retrieves time-stamped industrial data from equipment, SCADA, MES, PLCs, sensors, and control systems. It is designed to store high-frequency operational data and make it accessible for monitoring, analytics, reporting, and operational optimization.

Common use cases:

• Equipment and process monitoring
• Predictive maintenance
• Production performance and OEE
• Asset and fleet operations
• Energy consumption and optimization
• Anomaly detection and root-cause analysis

Legacy historians often struggle when industrial data strategies mature. Common challenges include:

• Proprietary storage formats
• Limited scalability for high-resolution data
• Difficult cloud/edge integration
• Lack of flexibility for semi-structured or unstructured data
• Slow access to granular historical records
• High licensing and expansion costs
• Limited support for advanced analytics or AI workloads

Modern Industry 4.0 and digital transformation programs require a historian that works across OT and IT, natively supports cloud and edge deployments, and integrates easily with AI/ML pipelines.

CrateDB is designed for the next generation of industrial data workloads. It combines time-series performance with multi-model flexibility, delivering ingestion, storage, and analytics in one distributed SQL engine. Key capabilities are:

High-frequency ingestion: Capture data from sensors, equipment, logs, and control systems at high throughput.

Instant query availability: Automatic indexing ensures that new data is ready for analytics within milliseconds.

Support for all industrial data types: Store time-series, relational, JSON, logs, text, geospatial, vector, and BLOB data in the same engine.

Fast analytics on large datasets: Run queries, aggregations, joins, search, and AI workloads on years of high-resolution data.

Flexible SQL interface: Use standard SQL without proprietary query languages or tools.

Horizontal scalability: Add nodes to increase capacity and maintain performance as data volumes grow.

Edge, on-premise, or cloud deployment: Same engine, full performance, in any environment.

AI-ready architecture: Feed high-quality historical and real-time data directly into AI and predictive maintenance models.

Modern industrial architectures require solutions that work seamlessly across both OT and IT domains. CrateDB provides a unified data foundation that:

• Connects to OT systems on the shop floor
• Integrates with IT systems in the cloud and data center
• Handles structured and unstructured data from both sides
• Supports AI, ML, BI, and enterprise analytics
• Delivers consistent performance from edge to cloud

This convergence simplifies architectures, reduces integration overhead, and unlocks holistic insights across the entire operation.

CrateDB integrates with Crosser, a hybrid-first streaming and integration platform designed for industrial environments.

Crosser enables:

• Direct connectivity to PLCs, SCADA, MES, sensors, and equipment
• Support for industrial protocols (OPC-UA, Modbus, MQTT, etc.)
• Edge nodes that run on-site behind the firewall
• Low-code data flows for filtering, enrichment, transformation
• Real-time stream analytics at the edge
• OT-to-IT data pipelines delivering cleaned operational data into CrateDB

Together, Crosser and CrateDB deliver a complete OT ingestion pipeline: from machine signals to real-time analytics and AI.

For organizations that need contextualized, governed, AI-ready operational data, CrateDB partners with EOT.AI.

EOT.AI provides:

• No-code/low-code pipelines that extract data from SCADA, historians, legacy OT systems
• A semantic layer that models assets, hierarchies, and metadata
• Unification of operational data with business and contextual data
• Data governance, quality, lineage, and access control
• AI-ready data products combining time-series, events, metadata, and context
• Integration with cloud analytics platforms, BI tools, and ML pipelines

CrateDB becomes the scalable storage and analytics engine for these enriched datasets, enabling predictive maintenance, digital twins, anomaly detection, and plant-wide optimization.

CrateDB + EOT.AI + Crosser together form a complete industrial data stack:
OT connectivity → data fabric & semantic modeling → scalable historian storage → analytics & AI.

CrateDB integrates ingestion, storage, and analytics into a fault-tolerant distributed architecture.

Ingestion:

• MQTT, Kafka, Flink, REST, IoT gateways, batch imports
• OPC-UA, Modbus, SCADA, MES, PLCs (via Crosser or EOT.AI)
• High-throughput, low-latency pipelines
• Automatic indexing and dynamic schemas

Storage

• Optimized for time-series
• Up to 70% data compression
• Handles structured, semi-structured, and unstructured data
• Automatic sharding and replication

Analytics

• Sub-second queries on large time-series datasets
• Aggregations, downsampling, trend analysis
• Vector search for anomaly detection and AI
• Text search for logs and contextual data
• Real-time dashboards, monitoring, and alerting tools

CrateDB fits naturally into industrial architectures. It connects with:

• SCADA / MES / PLCs (via Crosser or EOT.AI)
• Industrial gateways (via Crosser or EOT.AI)
• Cloud IoT hubs
• BI tools
• AI/ML platforms

It can serve as:

• A primary historian
• A scalable extension to an existing historian
• A unified data hub combining historian, IoT, and contextual IT data
• A foundation for AI and predictive analytics initiatives

Many industrial organizations rely on the AVEVA PI System for operational data collection. As deployments grow, teams often encounter limits around scale, proprietary formats, analytics flexibility, cloud integration, or total cost of ownership.

CrateDB offers a modern path forward: it can replace traditional historians such as AVEVA PI when organizations need a scalable, open, and cost-efficient platform for high-resolution time-series data, advanced analytics, and AI workloads. Many companies keep their existing OT data collection layer (PLCs, SCADA, OPC connectors, or PI interfaces) while adopting CrateDB as the new long-term historian and analytics backbone.

CrateDB is especially effective in PI modernization scenarios where teams want to:

• Move beyond storage bottlenecks
• Retain high-resolution data cost-effectively
• Run advanced SQL analytics across operational and contextual data
• Power AI and predictive maintenance models
• Integrate historian data with cloud, BI, and data platforms
• Consolidate data silos into a unified storage and analytics engine
• Support hybrid edge-to-cloud architectures

CrateDB does not aim to replicate proprietary PI functions. Instead, it provides a more flexible, scalable, and open backbone for long-term historical data, analytics, and AI — enabling a smooth transition from legacy historians to a modern data platform.

CrateDB runs everywhere with the same reliability and performance:

• On-premise for regulated environments
• Edge for real-time local processing
• Cloud for elastic scalability
• Hybrid deployments combining any of the above

This deployment flexibility supports any industrial or IoT architecture.

• Real-time visibility into operations
• High-resolution storage at lower cost
• Unified OT + IT data foundation
• Semantic modeling and AI readiness via EOT.AI
• Reliable OT connectivity via Crosser
• Faster analytics without ETL
• Open SQL interface
• Ideal for predictive maintenance and digital twins